Developing a Model for the Relationship Between Vegetation and Wind Power Using Remote Sensing and Geographic Information Systems Technology

Cetin M., Aksoy T., Bilge Ozturk G., Cabuk A.

Water, Air, and Soil Pollution, vol.233, no.11, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 233 Issue: 11
  • Publication Date: 2022
  • Doi Number: 10.1007/s11270-022-05887-0
  • Journal Name: Water, Air, and Soil Pollution
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, Compendex, EMBASE, Environment Index, Geobase, Greenfile, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: CORINE, Land degradation, NDVI, Remote sensing, Spatial planning
  • Anadolu University Affiliated: No


The aim of this study is to monitor the change of the existing vegetation in the area after the construction of wind power plants (WPPs) by using remote sensing (RS) and geographic information systems (GIS) technologies. The effects of WPPs on green areas have not been fully explored. The aim of this study is to expand on current knowledge and create a diagnostic model that shows the relationship between the turbines and the surrounding vegetation. All inventory data obtained within the scope of this study were compiled in GIS, and their relations in the field, the establishment dates, and numbers of WPPs were revealed. In this study, NDVI method was preferred. As a result, some negative changes were found in terms of slope and aspect according to CORINE (broadleaf forest degradation (BLF) and agricultural lands with natural vegetation degradation (NVA)) classes. While this study is a pioneer for studies in which WPP-related deterioration is made in terms of slope and aspect, it reveals the importance of GIS and RS. At the same time, using the Python programming language in common in GIS studies shows that making calculations with bulk data makes it easier to work in relatively large areas, especially for landscape planning studies.